Confidence Interval/Band for a Loess to Replicate geom_smooth

Question

I would like to get the upper and lower limits of the confidence interval for each observation in loess function to replicate what ggplot does in the geom_smooth()

library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = hp)) +
  geom_point() +
  geom_smooth(method = 'loess')

I know I can get the upper and lower bounds from linear models, but this is not available for loess:

lm_mod <- lm(hp ~ mpg, mtcars)
predict(lm_mod, mtcars, interval="confidence", level=0.95)

loess_mod <- loess(hp ~ mpg, mtcars)
predict(loess_mod, mtcars, interval="confidence", level=0.95)

Answer 1

Figured it out! The confidence intervals can be calculated from the standard errors which can be added prediction object using the se = TRUE argument. 1.96 standard deviations equates to a 95% confidence interval (with a normal distribution and hence assuming normality in the errors).

loess_mod <- loess(hp ~ mpg, mtcars)
pred <- predict(loess_mod, mtcars, se=TRUE)
mtcars1$lwl <- pred$fit-1.96*pred$se.fit
mtcars1$upl <- pred$fit+1.96*pred$se.fit

library(ggplot2)
ggplot(mtcars1, aes(x = mpg, y = hp)) +
  geom_point() +
  geom_smooth(method = 'loess') +
  geom_line(aes(y = lwl), color = "red") +
  geom_line(aes(y = upl), color = "red")

Hope this helps someone else.